Small Object Detection in Aerial Imagery using RetinaNet with Anchor Optimization

Deep Learning has successfully solved many computer vision problems sometimes in conjunction with traditional computer vision methods and sometimes by replacing them. In this paper, we aim to solve the problem of object detection by employing different methods from deep learning as well as computer vision. Significant amount of work is done in the domain of generic object detection, where usually objects (foreground) cover majority of image space as compared to background. In this paper we will focus on detecting small objects which constitute a tiny area as compared to background such as aerial imagery where desired objects such as people, cars etc. tend to appear relatively small. Such images have an intrinsic imbalanced class problem because background samples dominate object samples. We propose to use an anchor optimization method which will help reduce unnecessary region proposals as well as it can generate customized anchors depending upon the dataset. It can be used in conjunction with any single stage object detection framework. Its empirically noted that this anchor optimization technique improves accuracy over baseline frameworks.